When the translated texts and the training data come from the same domain, SMT systems can achieve good performance, otherwise the translation quality degrades dramatically.

it is of significant importance to develop translation systems which can be effectively transferred from one domain to another, for example, from newswire to weblog.


For example, "bank" often occurs in the sentences related to the economy topic when translated into "yínháng", and occurs in the sentences related to the geography topic when translated to "héàn".

the co-occurrence frequency of the phrases in some specific context can be used to constrain the translation candidates of phrases.


To obtain the corresponding lexical weight, we first estimate a lexical translation probability distribution w(e|f ) by relative frequency from the training corpus: Retaining the alignmentã between the phrase pair (f ,ẽ), the corresponding lexical weight is calculated as However, the above-mentioned method only counts the co-occurrence frequency of bilingual phrases, assuming that the translation probability is independent of the context information.

the statistical model estimated from the training data is not suitable for text translation in different domains, resulting in a significant drop in translation quality.


To solve this problem, we introduce the topic mapping probability P (t f out |t f in ) to map the in-domain phrase-topic distribution into the one in the out-domain topic space.

to be specific, we obtain the out-of-domain phrase-topic distribution P (t f out |f ) as follows: formula (4) can be further refined as the following formula: Next we will give detailed descriptions of the calculation methods for the three probability distributions mentioned in formula (6).


Existing models cannot be powerful enough to deal with such a rapidly growing search space.

decoding at very high DLs is not a good solution for these language pairs.


q(x i ) can be computed efficiently using inside-outside.

we can perform an EM-like alternation between calculatingα andθ.


It can be effectively constructed and the maximal rank of the nonterminals only increases by 1.

simple context-free tree grammars strongly lexicalize tree adjoining grammars and themselves.


Since a lexical item is a letter of the string alphabet, each production of a lexicalized grammar produces at least one letter of the generated string.

lexicalized grammars offer significant parsing benefits (Schabes et al., 1988) as the number of applications of productions (i.e., derivation steps) is clearly bounded by the length of the input string.


This is largely owing to the fact that our system aligns "jaxon 1 1 " with "Jaxson 1 2 " in the tweet "• • • I love Jaxson 1 2 ,Hes like my little brother• • • ", in which "Jaxson 1 2 " is identified as a PERSON.

this encourages our system to consider "jaxon 1 1 " as a PERSON.


In general we could use n multinomials to represent refinements and m multinomials to represent background text, but in this paper we simply use n = m = 1.

unsupervised learning in S-Mix can be viewed as clustering the segments with two latent states.


we extend S-HMM to include a latent label variable y for each review that takes values yes (contains refinement) and no (does not contain refinement).


On the other hand, we do not believe the textual content of the background segments in a y = yes review should be different from those in a y = no review.

the emission distributions are shared between the two HMMs, p(x j |z j , y; θ) = p(x j |z j ; θ).


Foulds and Smyth (2011) propose a generative model for MIL in which the generation of the bag label y is conditioned on the instance labels z.

of this setup, their model reduces to our S-Mix baseline in a fully unsupervised setting.


It predicts that 76.2% of reviews contain refinements, and additionally that 40.9% of segments contain refinements, whereas the true values are 57.8% and 23.2%, respectively.

these models provide high recall but low precision.


It also encourages the segment multinomials to focus modeling effort on words that appear only in the reviews.

in addition to yielding alignments, RSA-MixHMM provides small improvements over RS-MixHMM (though they are not statistically significant).


Annotating ground truth alignments is challenging and time-consuming due to ambiguity, and we feel that the alignments are best evaluated via a downstream task.

we leave thorough evaluation of the quality of the alignments to future work.


However, labeled resources in other languages are often insufficient or even unavailable.

it is desirable to use the English labeled data to improve sentiment classification of documents in other languages.


In fact, it is often the case, that a complex multi-sentence question could be answered from multiple aspects by different people focusing on different sub questions.

we address the incomplete answer problem by developing a novel summarization technique taking different sub questions and contexts into consideration.


One answer sentence may related to more than one sub questions to some extent.

we define the replied question Qr i as the sub question with the maximal similarity to sentence x i : It is intuitive that different summary sentences aim at answering different sub questions.


Meanwhile, other local and non-local sentences which are similar to it at above the upper threshold will probably not be selected as summary sentences as they offer similar viewpoint as discussed above.

we propose the following two kinds of contextual factors for selecting the answer sentences in the CRF model.


Since the ROUGE measures care more about the recall and precision of N-grams as well as common substrings to the reference summary rather than the whole sentence, they offer a better measurement in modeling the user's information needs.

the improvements in these measures are more encouraging than those of the average classification accuracy for answer summarization.


To support counting, the algorithm additionally records for each subtree a list (called occurrence list) of all trees in which this subtree occurs and of its position in the tree (represented by the list of tree nodes mapped onto by the subtree).

for a given subtree t, the support of t is the number of elements in that list.


Second, while the HTM algorithm uses support to prune the search space (only trees with a minimum support bigger than the set threshold are stored), the ETM algorithm drastically prunes the search space by additionally checking that the suspicion rate of all subtrees contained in a new tree t is smaller or equal to the suspicion rate of t .

while ETM looses the space advantage of HTM by a small margin 4 , it benefits from a much stronger pruning of the search space than HTM through suspicion rate checking.


Mining on Dependencies When mining on dependencies, suspects can point to syntactic constructions (rather than words or word categories) that are not easily spotted when mining on words or parts of speech.

while problems with coordination could easily be spotted through a high suspicion rate for the CC POS tag, some constructions are linked neither to a specific POS tag nor to a specific word.


This is done as follows (Deerwester et al., 1990;Bellegarda, 2000), again with the objective of minimizing the reconstruction error.

first, note that since U is column-orthonormal, (1) implies that if we notionally index a new document by p, we proceed by forming a new column (document) vector W p using the standard term-weighting, and then find its LSA-space representation V p using (2).


At the m-th iteration in Algorithm 1, iterative Viterbi decoding requires order of T 4 m time because there are 2 m active labels (plus one degenerate label).

it has m i=0 T 4 i time complexity if it terminates at the m-th iteration.


Efficient pruning contributes significantly to speeding up decoding.

to have a tighter (higher) lower bound lb is important.


We denote the number of such unique permutations of S R by n(S R ).

2 the probability of each permutation of S R is 1 3 .


In sum, we treat the unsupervised semantic role labeling task as clustering of argument keys.

argument occurrences in the corpus whose keys are clustered together are assigned the same semantic role.


This may cause the beam buffer to be filled only with the states, and these may exclude other states, such as scanned or completed states.

we limit the number of predicted states from a single state by prediction size implicitly in line 10 of Algorithm 1.


In step 0, topdown parser predicts a child node, a root node of a complete tree, using little syntactic information, which may lead to errors in the root node selection.

we think that it is important to seek more suitable features for the prediction in future work.


Since G has no useless nonterminals, for each nonterminal A of G, there is a derivation tree for some string in L(G) in which A appears in a node label.

by Lemma 1, there are strings A decomposition of w ∈ Σ * is a finite binary tree satisfying the following conditions: • the root is labeled by some (w 1 , w 2 ) such that w = w 1 w 2 , • each internal node whose left and right children are labeled by (u 1 , u 2 ) and (v 1 , v 2 ), respectively, is labeled by one of (u 1 u • each leaf node is labeled by some (s 1 , the label of an internal node in a decomposition is obtained from the labels of its children by left concatenation, right concatenation, or wrapping.


the condition ( †) holds with i + 1 in place of i.


Since ψ 2 (v i,1 v i,2 ) ∈ [−2, 2], it follows that v i,1 must contain at least one occurrence of b.

ba 19 c 17 must be a substring of v i,1 = w i+1,1 , which shows that ( †) holds with i + 1 in place of i.


At present, learning from one single treebank seems inadequate for further boosting parsing accuracy.

1  studies have recently resorted to other resources for the enhancement of parsing models, such as large-scale unlabeled data (Koo et al., 2008;Chen et al., 2009;Bansal and Klein, 2011;Zhou et al., 2011), and bilingual texts or cross-lingual treebanks (Burkett and Klein, 2008;Burkett et al., 2010;Chen et al., 2010).


Despite the divergence of annotation philosophy, these treebanks contain rich human knowledge on the Chinese syntax, thereby having a great deal of common ground.

exploiting multiple treebanks is very attractive for boosting parsing accuracy.


In a political blogs dataset, the mentions refer to political figures in the United States (e.g., Mrs. Obama and Michelle Obama).

the model discovers parts of names-Mrs., Michelle, Obamawhile simultaneously performing coreference resolution for named entity mentions.


For every row in the table, we have a multinomial distribution over context vocabulary θ r from a Dirichlet prior λ.

the probability of observing the tth context word for mention m is p(s where N −mt r is the number of context words of mentions assigned to row r, N −mt rs is the number of context words of mentions assigned to row r that are s mt , both excluding the current context word, and v ranges over the context vocabulary of size V .


Furthermore, in addition to the devices stated by copywriters, there might be other latent methods that these experts unconsciously use.

we consider the task of discovering and accumulating all crucial features of creativity to be essential before attempting to automatize the naming process.


The name Caterpillar, which is an earth-moving equipment company, is used as a metaphor.

we need extra information regarding the domain description in addition to the names.


Although our system is actually able to produce a limitless number of results for a given input, we limited the number of outputs for each input to reduce the effort required for the annotation task.

we implemented a ranking mechanism which used a hybrid scoring method by giving equal weights to the language model and the normalized phonetic similarity.


We take each sense cluster of a pattern as an atomic cluster, and use hierarchical agglomerative clustering to organize them into semantic relations.

a semantic relation comprises a set of sense clusters of patterns.


Considering the kind of corpus we deal with (news), we decided to consider TEs whose granularity is at least equal to a day.

tEs were normalized to a numerical YYYYMMDD format (where YYYY corresponds to the year, MM to the month and DD to the day).


Because of the importance of interpretability for social science research, the choice of vocabulary size is critical to ensure understandable topics.

we report our results at various vocabulary sizes W on SME and SAGE.


We don't want to penalize these generic rules.

we further propose a topic sensitivity model which rewards generic rules so as to complement the topic similarity model.


The k-th dimension P (z = k|d) means the probability of topic k given document d. Different from rule-topic distribution, the documenttopic distribution can be directly inferred by an offthe-shelf LDA tool.

based on these two distributions, we select a rule for a document to be translated according to their topic similarity (Section 3.1), which measures the relatedness of the rule to the document.


comparing with the previous topic-specific lexicon translation method, our method provides a more efficient way for incorporating topic model into SMT.


The intuition under the above setting is that shared documents between categories create semantic links between them.

if we remove common documents between father and children, we reduce the dependencies that can be captured with traditional bag-of-words representation.


However, once grandparents are included in the factorization, the dpo3+Unlabeled is significantly better at attaching conjunctions than the constituency parser, attaching conjunctions with an accuracy over 90%.

some of the disadvantages of dependency parsing compared with constituency parsing can be compensated for with larger factorizations.


2 Fine-grained IS is a prerequisite to full bridging/comparative anaphora resolution, and therefore necessary to fill gaps in entity grids (Barzilay and Lapata, 2008) based on coreference only.

examples 1 and 2 do not exhibit any coreferential entity coherence but coherence can be established when the comparative anaphor others is resolved to others than freeway survivor Buck Helm, and the bridging anaphor the streets is resolved to the streets of Oranjemund, respectively.


IS is said to influence word order (Birner and Ward, 1998;Cahill and Riester, 2009) and this fact has been exploited in work on generation (Prevost, 1996;Filippova and Strube, 2007;Cahill and Riester, 2009).

we integrate dependencies between the IS classification of mentions in precedence relations.


We notice that existing approaches for PU learning are not likely to scale given the restrictions imposed by streaming data.

we propose highly incremental approaches, which are able to process large-scale streaming data.


Clearly, other tricks like this one can be used, but, unfortunately, they do not guarantee the absence of false-positives, and they are not complete since the majority of messages mentioning the entity name may appear outside its profile.

the collected examples are not totally reliable, and disambiguation performance would be seriously compromised if classifiers were built upon these uncertain examples directly.


In this paper we hypothesize that it is worthwhile to enhance the reliability of unlabeled examples, provided that this type of data is inexpensive and the enhancement effort will be then rewarded with an improvement in disambiguation performance.

we propose a new approach based on the Expectation-Maximization (EM) algorithm (Dempster et al., 1977).


In this case, well-established classifiers, such as SVMs (Joachims, 2006), have to be learned entirely from scratch, replicating work by large.

we propose as an alternative the use of Lazy Associative Classifiers (Veloso et al., 2006).


We denote as R x the set of rules in R that are applicable to example x.

only and all the rules in R x are considered as valid votes when classifying x.


Initially R is empty; a subset R x i is appended to R every time an example x i is processed.

after processing a sequence of m examples Before extracting rule {X → c}, it is checked whether this rule is already in R. In this case, while processing an example x, if an entry is found with a key matching {X , c}, then the rule in R is used instead of extracting it from D x .


For example, with a large enough corpus, a distant supervision system may find that patterns in the sentence S1 strongly correlate with seed facts of birthPlace whereas patterns in S2 do not qualify as a strong indicator.

intuitively the quality of distant supervision should improve as we use larger corpora.


One problem is that relations in D are defined at the entity level.

the pairs in such relations are not embedded in text, and so these pairs lack the linguistic context that we need to extract features, i.e., the features used to describe examples.


Just as direct training data are scarce, ground truth for relation extraction is scarce as well.

prior work mainly considers two types of evaluation methods: (1) randomly sample a small portion of predictions (e.g., top-k) and manually evaluate precision/recall; and (2) use a held-out portion of seed facts (usually Freebase) as a kind of "distant" ground truth.


One reason is that human labels acquired from crowdsourcing have comparable noise level as distant labels -as shown by Figure 4.

techniques that improve the accuracy of crowd-sourced answers are an interesting direction for future work.


In this task, a particular realization t already uniquely defines a particular segmentation (mention boundaries) of the event span, thus the h only contributes type information to t. As we will discuss in Section 2.3, only a collection of local features are defined.

a Viterbi-style dynamic programming algorithm is used to efficiently compute the desired solution.


This formulation gives us a complete flexibility to assign arbitrary structured preferences, where positive scores can be assigned to good properties, and negative scores to bad ones.

in this way, the quality of a complete structure is jointly measured with multiple different property functions.


For ease of exposition, we will illustrate the problem using a special case of the SS framework where the sentences are concept definitions in a dictionary such as WordNet (Fellbaum, 1998) (WN).

the sentence corresponding to the concept definition of bank#n#1 is a sparse vector in X containing the following observed words where X ij = 0: the 0.1, f inancial 5.5, institution 4, that 0.2, accept 2.1, deposit 3, and 0.1, channel 6, the 0.1, money 5, into 0.3, lend 3.5, activity 3 All the other words (girl, car,..., check, loan, business,...) in matrix X that do not occur in the concept definition are considered missing words for the concept entry bank#n#1, thereby their X ij = 0 .


PLSA assumes each document has a distribution over K topics P (z k |d j ), and each topic has a distribution over all vocabularies P (w i |z k ).

pLSA finds a topic distribution for each concept definition that maximizes the log likelihood of the corpus X (LDA has a similar form): In this formulation, missing words do not contribute to the estimation of sentence semantics, i.e., excluding missing words (X ij = 0) in equation 1 does not make a difference.


Moreover, the true semantics of the concept definitions is actually related to some missing words, but such true semantics will not be favored by the objective function, since equation 2 allows for too strong an impact byX ij = 0 for any missing word.

the LSA model, in the context of short texts, is allowing missing words to have a significant "uncontrolled" impact on the model.


Our models learn the salience of each social cue in establishing reference, relative to their co-occurrence with objects that are not being referred to.

this work is consistent with a view of language acquisition in which children learn to learn, discovering organizing principles for how language is organized and used socially (Baldwin, 1993;Hollich et al., 2000;Smith et al., 2002).


(to appear) show that for the current corpus, the topic of the preceding utterance is very likely to be the topic of the current one also.

the child's eyes might be a good predictor because they reflect the fact that the child's attention has been drawn to an object by previous utterances.


Recall that our goal is to identify effects based in the language of the quotes themselves, beyond any factors arising from the speaker or context.

for each (singlesentence) memorable quote M , we identify a nonmemorable quote that is as similar as possible to M in all characteristics but the choice of words.


Table 2 shows that all the subjects performed (sometimes much) better than chance, and against the null hypothesis that all subjects are guessing randomly, the results are statistically significant, p < induced labels with some reliability.

6  far we have discussed a dataset in which memorability is determined through an explicit labeling drawn from the IMDb.


In the end, we found that it was more effective to use the result counts in conjunction with the IMDb labels, so that the counts played the role of an additional filter rather than a free-standing numerical value.

for each pair (M, N ) produced using the IMDb methodology above, we searched for each of M and N as quoted expressions in a Boolean conjunction with the title of the movie.


This suggests that conventional lexical and phrasal translation models adopted in those SMT systems are not sufficient to correctly translate predicates in source sentences.

we propose a discriminative, feature-based predicate translation model that captures not only lexical information (i.e., surrounding words) but also high-level semantic contexts to correctly translate predicates.


We observe on our training data that the number of arguments for 96.5% verbal predicates on each side (left/right) is not larger than 3.

the defined 6-argument semantic window is sufficient to describe argument contexts for predicates.


Since the predicate determines what arguments are involved in its semantic frame and semantic frames tend to be cohesive across languages (Fung et al., 2006), the movements of predicate and its arguments across translations are like the motions of a planet and its satellites.

we consider the reordering of an argument as the motion of the argument relative to its predicate.


This shows that not only verbal predicates are semantically important, they also form a major part of the sentences.

whether verbal predicates are translated correctly or not has a great impact on the translation accuracy of the whole sentence 7 .


The maximum flow equivalence breaks down when the c(•) variables are introduced, so in the general case, replacing max with sum is only an approximation.

returning to our sample problem, the linear programming solver simply needs to assign: due to the maximum covering weights constraint, we can give the following value assignment, implying that all n-grams have been matched.


• TESLA-CELAB can process word-level synonyms and can award character-level matches.

the difference between TESLA-M and TESLA-CELAB highlights the contribution of character-level matching, and the difference between TESLA-CELAB− and TESLA-CELAB highlights the contribution of word-level synonyms.


We constrain the forbidden types of relation to become one-to-one, as in (Birch and Osborne, 2011).

in a one-tomany alignment, the single source word is forced to align with the first target word; in a many-to-one alignment, monotone order is assumed for the target words; and source words originally aligned to null are aligned to the target word position just after the previous source word's target position.


The human judgment score is based on the "Rank" only, i.e., how often the translations of the system were rated as better than those from other systems (Callison-Burch et al., 2008).

bLEU, METEOR, and PORT were evaluated on how well their rankings correlated with the human ones.


E.g., for zh-en small, the BLEU-tuned system produced 44,677 1-grams (words), while the Qmean-trained system one produced 43,555 1grams; both have about 32,000 1-grams matching the references.

the Qmean translations have higher precision.


However, language model adaptation is a more straight-forward problem compared to translation model adaptation, because various measures such as perplexity of adapted language models can be easily computed on data in the target domain.

language model adaptation has been well studied in various work (Clarkson and Robinson, 1997;Seymore and Rosenfeld, 1997;Bacchiani and Roark, 2003;Eck et al., 2004) both for speech recognition and for machine translation.


In the typical log-linear model SMT, the posterior probability for each phrase pair (ē,f ) is given by: Ensemble decoding uses the same framework for each individual system.

the score of a phrase-pair (ē,f ) in the ensemble model is: where ⊕ denotes the mixture operation between two or more model scores.


Since in log-linear models, the model scores are not normalized to form probability distributions, the scores that different models assign to each phrasepair may not be in the same scale.

mixing their scores might wash out the information in one (or some) of the models.


We treat shallow parsing as a sequence label task, and a sentence f can have many possible different chunk label sequences.

in theory, the conditional probability of a target translation e conditioned on the source sentence f is given by taking the chunk label sequences as a latent variable c: In practice, we only take the best chunk label sequenceĉ got byĉ Then we can ignore the conditional probability p(ĉ|f ) as it holds the same value for each translation, and get: We formalize our model as a weighted SCFG.


Moreover, each text has the tendency to maximally use its own language and avoid vocabulary from other languages.

uDHRderived results can be considered to provide an empirical upper bound on our formulation.


Second, the sizes of text portions in multilingual web texts differ greatly, which would make it difficult to evaluate the overall performance of the proposed method in a uniform manner.

we artificially generated two kinds of test sets from a monolingual corpus.


Although language change within the middle of a word does occur in real multilingual documents, it might seem more realistic to consider language change at word borders.


Although there are web pages consisting of texts in more than 2 languages, we rarely see a web page containing 5 languages at the same time.

test 1 reflects the most important case of 2 languages only, whereas test 2 reflects the case of multiple languages to demonstrate the general potential of the proposed approach.


Instead, as emphasized by the above examples, some of them lie in the sentiment dictionaries, while others lie in WordNet.

a by-product of our polarity consistency analysis is that it can also locate some of the likely places where WordNet needs linguists' attention.


Also, the clauses in the MUC appear in Φ ′ .

φ ′ is unsatisfiable and the words in W ′ are inconsistent.


This is expected for AL because it has only two nouns and no verb, while Q-WordNet has only 40 adverbs.

these two dictionaries have limited "overlay".


Explicit relation and Non-Explicit relation have different distributions on each discourse relation (PDTB-Group, 2007).

adding this information may further improve the model.


In the TAC summarization track, human judges scored each model and candidate summary with a readability score from 1 to 5 (5 means most readable).

in our setting, instead of a pair of texts, the training input consists of a list of model and candidate summaries from each topic, with their annotated scores as the rankings.


Since zero-one loss considers all errors equally, it can not distinguish error types.

a new loss is required to incorporate different error types into the learning machines.


They basically assign less value for intra-category errors than inter-category errors.

a classifier is optimized to strictly keep inter-category errors within a smaller boundary.


As shown in Table 5, both CL-MSVM and TL-MSVM achieve lower accuracies than other methods, while their inter-category errors are less than that of other experimental methods.

the improvement of CL-MSVM and TL-MSVM implies that, for the subsequent natural language processing, a POS tagger should considers different cost of tagging errors.


An intercategory error occurs by mislabeling a word with a totally different tag, while an intra-category error is caused by a similar POS tag.

intercategory errors affect the performances of subsequent NLP tasks far more than intra-category errors.


Most supervised machine learning techniques are optimized on their loss functions.

the performance of POS taggers based on supervised machine learning techniques can be improved by applying the proposed loss functions to learn their classifiers.


In contrast, because the Chinese language does not have spaces between words, we fundamentally need to consider the lattice structure of the whole sentence.

we place no restriction on the segmentation possibilities to consider, and we assess the full potential of the joint segmentation and dependency parsing model.


In beam search, we use the step index that is associated with each state: the parser states in process are aligned according to the index, and the beam search pruning is applied to those states with the same index.

for the beam search to function effectively, all states with the same index must be comparable, and all terminal states should have the same step index.


Because segmentation using a dictionary alone can serve as a strong baseline in Chinese word segmentation (Sproat et al., 1996), the use of dictionaries is expected to make our joint model more robust and enables us to investigate the contribution of the syntactic dependency in a more realistic setting.

we optionally use four features D01-D04 associated with external dictionaries.


tagging (Zhang and Clark, 2008;Zhang and Clark, 2010) and dependency parsing (Huang and Sagae, 2010).

we can investigate the contribution of the joint approach through comparison with the pipeline and joint models.


High-quality constraints can be learned with respect to a special tagset, however, with this tagset, the best segmentation accuracy is hard to achieve.

these character-based constraints are not directly used for determining predictions as in English POS tagging.


However, when tagset BMES is used, the learned constraints don't always make reliable predictions, and the overall precision is not high enough to constrain a probabilistic model.

we will only use the deterministic constraints that predict IB tags in following CWS experiments.


This semantically-oriented representation is intuitive and easy to apply, requiring little linguistic expertise.

stanford dependencies are widely used: in biomedical text mining (Kim et al., 2009), as well as in textual entailment (Androutsopoulos and Malakasiotis, 2010), information extraction (Wu and Weld, 2010;Banko et al., 2007) and sentiment analysis (Meena and Prabhakar, 2007).


However, since the default classifier used by Malt-Parser is libsvm (Chang and Lin, 2011) with a polynomial kernel, it may be too slow for training models on all of CTB 7.0 training data in acceptable time.

we also tested this particular parser with the faster liblinear (Fan et al., 2008) classifier.


4 We note that these smoothing parameters are tailored to short sentences: in a binary tree, the number of constituents grows linearly with sentence length, whereas the number of distituents grows quadratically.

the ratio of constituents to distituents is not constant across sentence lengths.


An SMT decoder is deployed by the user, with all the required resources to operate except the PT 1 .

of following the proposed procedure: • The user acquires all and only the phrase table entries required to perform the decoding of a specific file, thus avoiding complete transfer of the TM to the user; • The owner of the PT does not learn anything about what is being translated, thus satisfying the user's confidentiality constraints; • The owner of the PT can track the number of phrase-table entries that was downloaded by the user.


Rather than use a wildcard * in both positions (ADJ and NOUN, or VERB and NOUN), which gives limited results with a search engine like Google, we generate fully instantiated similes from hypotheses generated via the Google n-grams (Brants & Franz, 2006).

from the 3-gram "a drooling zombie" we generate the query "drooling like a zombie", and from the 3-gram "a mindless zombie" we generate "as mindless as a zombie".


For a property p, we define N + (p) and N -(p) as We assign pos/neg valence scores to each property p by interpolating from reference values to their neighbors in N. Unlike that of Takamura et al.

(2005), the approach is non-iterative and involves no feedback between the nodes of N, and thus, no inter-dependence between adjacent affect scores: (3) pos(p) = |N If a term S denotes a stereotypical idea and is described via a set of typical properties and behaviors typical(S) in the lexicon, then: (5) and (6) calculate the mean affect of the properties and behaviors of S, as represented via typical(S).


NBSVM performs well on snippets and longer documents, for sentiment, topic and subjectivity classification, and is often better than previously published results.

nBSVM seems to be an appropriate and very strong baseline for sophisticated methods aiming to beat a bag of features.


In general, BNB is less stable than MNB and performs up to 10% worse.

benchmarking against BNB is untrustworthy, cf.


We extend this model by adding specialized DPs for left and right auxiliary trees.

3 we have an exchangeable process for generating right auxiliary trees as for initial trees in TSG.


Computing the topic distribution over a document and assigning it to the sentences serves to tie the sentences together in the document context.

to obtain the lexical probability conditioned on topic distribution, we first compute the expected count e zn (e, f ) of a word pair under topic z n : where c j (•) denotes the number of occurrences of the word pair in sentence x j , and then compute: e e zn (e, f ) we will introduce 2•K new word translation tables, one for each p zn (e|f ) and p zn (f |e), and as many new corresponding features f zn (e|f ), f zn (f |e).


Evaluation based on exact match against a single reference answer leads to under-estimation, because an English phrase is often translated into several Chinese counterparts.

we asked a human judge to examine and mark the outputs of our full system.


Since observed words in definitions are too few to reveal the semantics of senses, missing words can be used to tell the model what the definition is not about.

we want to find a latent semantics profile that is related to observed words in a definition, but also not related to missing words, so that the induced latent semantics is unique for the sense.


Given only a few observed words in a definition, there are many hypotheses of latent vectors that are highly related to the observed words.

missing words can be used to prune the hypotheses that are also highly related to the missing words.


This is largely due to the fact that these algorithms often produce invalid rules.

evaluation is necessary both for resource developers as well as for inference system developers who want to asses the quality of each resource.


They also co-referenced multiple email addresses belonging to one person, and assigned unique identifiers and names to persons.

each person is apriori associated with a set of email addresses and names (or name variants), but has only one unique identifier.


The task of predicting if two people are  Table 1: Prediction accuracy by type of predicted organizational dominance pair; "Inter" means that one element of the pair is from the core and the other is not; a negative error reduction indicates an increase in error in a dominance relation is different and we do not address that task in this paper.

we restrict our evaluation to pairs of people (p 1 , p 2 ) who are related hierarchically (i.e., either DOM(p 1 ,p 2 ) or DOM(p 2 ,p 1 ) in the gold standard).


Furthermore, because of the network characteristics described above (a relatively dense network), the core people are also more likely to have a high centrality degree, as compared to the non-core people.

the correlation between centrality degree and hierarchal dominance will be high.


The number of such pairs is |T | = 2, 640.

if we consider a perfect NLP system that correctly predicts the dominance of 2, 640 tuples and randomly guesses the dominance relation of the remaining 11, 084 tuples, the system would achieve an accuracy of (2640 + 11084/2)/13724 = 59.61%.


In the second step, 6 annotators (different from the first step) were asked to pick the best one from the 8 compressions from the previous step.

for each sentence, we have 8 human compressions, as well a best one selected by the majority of the 6 annotators in the second step.


In this case, we can exploit global context to determine which correction is appropriate: the first sentence describes a past event, and the second sentence refers the first sentence.

the verb should be changed to past tense.


However, existing annotated corpora are limited in size, 2 which precludes the possibility of machine learning-based approach.

we constructed a large-scale tense/aspect corpus from Lang-8, 3 a social networking service for learners of foreign languages.


As we described in Section 1, using only local information about the target verb phrase may lead to inaccurate correction of tense/aspect errors.

we take into account global context: the relation between target and preceding/following verb phrases.


Significance tests are part of the pruning stage of the algorithm.

the number of triples eventually retrieved confirms the existence of statistically significant correlations between pairs of events and relation words.


However, factual knowledge is highly ephemeral: Royals get married and divorced, politicians hold positions only for a limited time and soccer players transfer from one club to another.

knowledge bases should be able to support more sophisticated temporal queries at entity-level, such as "Who have been the spouses of Prince Charles before 2000?"


By coupling both approaches we achieve both high(er) precision and high(er) recall.

our method efficiently extracts high quality temporal facts at large scale.


WordNet's definitions of the supersenses are terse, and we could find little explicit discussion of the specific rationales behind each category.

we have crafted more specific explanations, summarized for nouns in figure 2.


First, in DADT author topics are disjoint from document topics, with different priors for each topic set.

the number of author topics can be different from the number of document topics, enabling us to vary the number of author topics according to the number of authors in the corpus.


(2011) proposed CODA for adaptation based on co-training (Blum and Mitchell, 1998), which is however a pseudo multi-view algorithm where original data has only one view.

it is not suitable for the true multi-view case as ours.


Removing s and always computing s yields the first algorithm in Section 4.

algorithm 1 SaMPLING WITH REFINED BUCKET 1: for word w in this document do 2: sample = rand() * (s + r + q) 3: if sample < s then 4: compute s 5: sample = sample * (s + r + q)/(s + r + q) 6: if sample < s then 7: return topic k and path λ sampled from s 8: sample − = s 9: else 10: sample − = s 11: if sample < r then 12: return topic k and path λ sampled from r 13: sample − = r 14: return topic k and path λ sampled from q Sorting far, we described techniques for efficiently computing buckets, but quickly sampling assignments within a bucket is also important.


(2010) and Paul et al., (2010) further pointed out that coarser-grained segmentation of the source sentence do help capture more contexts in translation.

rather than using character, using coarser-grained, at least as coarser as the conventional word, as WSR is quite necessary.


Both methods iteratively learn word segmentation and alignment alternatively, with the former starting from word-based corpus and the latter starting from characters-based corpus.

pW can be experimented on all segmentations.


Observe that the HFE sentences can be regarded as binary trees annotated with syntax tags augmented with swap/straight suffixes.

the structures of these binary trees can be learnable by using an off-the-shelf grammar learning algorithm.


We assume that the polarity of document is closely related to some aspects for the reason that people are writing reviews to praise or criticize certain aspects.

each informative sentence of the document characterizes one aspect, expressing aspect specific polarity or subjective features.


To make prediction, we have the document-level sentiment classifier as where is a function that depends only on the number of sentences in x, as illustrated in (Yessenalina et al., 2010).

for each sentence x j , we compute the joint subjectivity and polarity score with respect to aspect a and label y as we then assign aspect a j to sentence x j if After sorting score(x j , a j , y) in decreasing order and taking summation by selecting the top f (|x|) (or fewer, if there are fewer than f (|x|) that have positive joint score) sentences as the total score for each y ∈ {+1,−1} , we then predict y with the higher joint score as the sentiment of the whole document.


For each polarity indicator, we define the "scope" (the minimal syntactic structure containing at least one subjective word) of each indicator for different representations as follows: For a constituent tree, a node and its children correspond to a grammatical production.

considering the terminal node tragic in the constituent structure tree in Figure 1(a), we extract the subtree rooted at the grandparent of the terminal, see Figure 2(a).


But their total numbers are 777 / 919 (SBJ) and 918 / 1211 (OBJ) and much fewer than A0 and A1 labels.

sR labels can connect much more arguments to their predicates.


Moreover, implementations of learning and inference methods are already provided in existing Markov Logic interpreters such as Alchemy 2 and Markov thebeast.

3 we can focus our effort on building a set of formulae called Markov Logic Network (MLN).


6In fact, Formula (5) gains a higher weight than Formula (6) by learning on training data.

our system gives "1-Harari" more chance to survive in compression.


From our observation of human-written summaries, it also does not require a great deal of originality to be considered excellent by human standards.

we have designed straightforward generation patterns for each scheme.


Even if the assessor is inconsistent in selecting Summary Content Units (SCUs) across different summaries, as long as the total summary weight is similar, the summary's final score will be similar, too.

2 it would be better to look at whether assessors tend to find the same SCUs (information "nuggets") in different summaries on the same topic, and whether they annotate them consistently.


However, the performance degrades increasingly as the discrepancy between the training and testing data gets larger.

to ensure robustness, a tagger needs to be evaluated on several different kinds of data.


Moreover, computing this correlation and finding the best value for the parameter (i.e., what the authors call the proper exponent) requires a manually segmented training corpus.

this proper exponent may not be easily available in all situations.


The average consistency is found to be as low as 0.85 (f-score).

this figure can be considered as a sensible topline for unsupervized systems.


In a segmented Chinese text, most of the tokens are uni-and bigrams but most of the types are bi-and trigrams (as unigrams are often high frequency grammatical words and trigrams the result of more or less productive affixations).

the results of evaluations only based on tokens do not suffer much from poor performances on trigrams even if a large part of the lexicon may be incorrectly processed.


The parameter estimation is carried out in the usual way on the above feature space.

the weights of the common features are emphasized if the features are consistent between the source and the target.


From these sentences, only particle errors were retained; the other errors were corrected.


In addition, it is straightforward to integrate n-gram language models into phrase-based decoders in which translation always grows left-to-right.

phrase-based decoders only need to maintain the boundary words on one end to calculate language model probabilities.


They suffice to operate on wellformed structures and produce projective dependency parse trees.

with dependency structures present in the stacks, it is possible to use dependency language models to encourage linguistically plausible phrase reordering.


TM is very useful for repetitive material such as updated product manuals, and can give high quality and consistent translations when the similarity of fuzzy match is high.

professional translators trust TM much more than SMT.


Thirdly, the pipeline approach does not utilize the SMT probabilistic information in deciding whether a matched TM phrase should be adopted or not, and which target phrase should be selected when we have multiple candidates.

the possible improvements resulted from those pipeline approaches are quite limited.


The integrated models then consider all corresponding TM target phrases and SMT preference during decoding.

the proposed integrated models combine SMT and TM at a deep level (versus the surface level at which TM result is directly plugged in under previous pipeline approaches).


The more similar the context are, the more likely that the translations are the same.

this NLN feature reflects the number of matched neighbors (words) and it is a vector of <x, y>.


It is introduced because the SCM and TCM for a sentence-end-punctuation are always "Same" regardless of other features.

it is used to distinguish this special case from other cases.


The SMT decoder then only translates the unmatched/marked source phrases and gets the desired results.

the inserted parts in the TM target sentence are automatically included.


Furthermore, Model-III is much better than Ma-11-U at most intervals.

it can be concluded that the proposed models outperform the pipeline approaches significantly.


We implement the proposed methods in our own framework RegAligner rather than GIZA++, which is only rudimentally maintained.

we compare to the deficient models in our own software as well as to those in GIZA++.


These variations result in time measurements that are not comparable across annotators.

far, the use of post-editing time has been done on an per-annotator basis (Specia, 2011), or simply averaged across multiple translators (Plitt and Masselot, 2010), both strategies far from ideal.


For example, if a bigram parameter is modified due to the presence of some set of trigrams, and then some or all of those trigrams are pruned from the model, the bigram associated with the modified parameter will be unlikely to have an overall expected frequency equal to its observed frequency anymore.

the resulting model degrades dramatically with pruning.


There are, in addition, two other important differences in our approach from that in Kneser and Ney (1995), which would remain as differences even if our target expected frequency were the unsmoothed relative frequency P instead of the smoothed estimate P. First, the sum in the numerator is over histories of length n, the highest order in the n-gram model, whereas in the Kneser-Ney approach the sum is over histories that immediately back off to h , i.e., from the next highest order in the n-gram model.

the unigram distribution is with respect to the bigram model, the bigram model is with respect to the trigram model, and so forth.


As stated earlier, the use of β(h wG w) in the numerator of equation 8 for h wG that are larger than h implies that the longer n-grams must be re-estimated first.

we process each history length in descending order, finishing with the unigram state.


If we do this, the 'Pruned+MDC' perplexities increase by between 4.5-5.6 points, i.e., about half of the perplexity reduction is lost for each method.

while iteration of denominator calculation may not be critical, it should not be treated as a constant.


Compositionality is handled by linking or coindexing the arguments of the conjoined HMMs.

a sentence like The person to the left of the backpack approached the trashcan would be represented as a conjunction of person(p 0 ), to-the-left-of(p 0 , p 1 ), backback(p 1 ), approached(p 0 , p 2 ), and trash-can(p 2 ) over the three participants p 0 , p 1 , and p 2 .


Sixth, we assume that we know the argument-to-participant mapping for each training sentence.

for example, for the above sentence we would know , and trash-can(p 2 ).


Also suppose that setting s i = 0 yields FN i false negatives.

then the F1 score is we want to minimize the term ∆.


But if an outscoping relation is mislabeled, it will change the interpretation of the sentence.

in MA11, we suggest defining a precision/recall based on the number of outscoping relations recovered correctly: 4 argues that TC-based metrics tend to produce higher numbers.


Since we feed each pair of elements to the classifiers independently, each (unordered) pair introduces one sample.

a sentence with n scopal elements creates C(n, 2) = n(n − 1)/2 samples for classification.


We define two versions of q(y g(i) , y h(i,k) ): It is notable that Place and Time arguments are applicable and behave similarly to all event subtypes.

features for these arguments are not conjuncted with trigger labels.


As we can see both curves converge around iteration 20 and the global features improve the overall performance, compared to its counterpart with only local features.

we set the number of iterations as 20 in the remaining experiments.


In particular, let us consider a rule Unlike syntactic parsing, child types of a parse tree uniquely define the parent type of the rule; this is a direct consequence of our combination rules being functions with domains defined in terms of the temporal types, and therefore necessarily projecting their inputs into a single output type.

the first class of bracketing featuresover types -reduce to have the exact same expressive power as the nonterminal CFG rules of Angeli et al.


When the coreference resolution system is used, the best accuracy value decreases while the insertion score increases from 0.114 to 0.138 (Table 4).

coreference resolution tends to associate positions that are closer to the original ones.


The poor performance of our system in this case can be explained by the fact that the coreference resolution system regroups more entities in Encyclopedia Britannica documents than in Britannica Elementary ones.

the number of entities that are "shared" by two sentences increases more importantly in the Encyclopedia Britannica corpus, while the dis-tance between two occurrences of one entity decreases in a more significant manner.


The majority label, by contrast, is only selected if it is the most likely prediction by all classifiers in our ensemble.

consensus is required to elect the majority class, and the strongest minority candidate is elected otherwise.


This factor graph grows quadratically in the size of the document, and even approximate inference becomes slow when a document contains over 200 mentions.

we use our BA-SIC model to prune antecedent choices for each a i in order to reduce the size of the factor graph that we must instantiate.


However, we found no simple way to change the relative performance characteristics of our various systems; notably, modifying the parameters of the loss function mentioned in Section 4 or changing it entirely did not trade off these three metrics but merely increased or decreased them in lockstep.

the TRANSITIVE system actually substantially improves over our baselines and is not merely trading off metrics in a way that could be easily reproduced through other means.


For example, "制 服" means "dominate" when it is tagged as a verb, of which the head is the left character; the same word means "uniform dress" when tagged as a noun, of which the head is the right character.

the input of the word structure annotation is a word together with its POS.


We have where σ 1 and σ 2 are as in the la k case, and we have set Transitions la k and ra k are parametric in k, where k is bounded by the length of the input string and not by a fixed constant (but see also the experimental findings in §5).

our system uses an unbounded number of transition relations, which has an apparent disadvantage for learning algorithms.


At the core of our approach is a binarization algorithm that can be adapted to a new formalism by changing a parameter at runtime.

it only needs to be implemented once, and can then be reused for a variety of formalisms.


Note that the terms h 1 (ξ) and h 1 (α) are equivalent in that they denote the same function over the string algebra, and so are the terms h 2 (ξ) and h 2 (α).

replacing the α-rule by the rules in Fig.


These operations do not change the variable tree; one can use this to show that t i has the same structure as ξ.

if we can find binarization terms, we can construct a binarization of r. Conversely, for any given binarization (ξ, h 1 , .


This term is equivalent to itself and to con 2 (x 1 , con 2 (x 2 , x 3 )), but the b-rules only cover the former.


Phrase-based SMT mainly 1 estimates word reordering using distortion models 2 .

distortion models are one of the most important components for phrase-based SMT.


The target word to be generated next will be "bought" and the source word to be selected next will be its corresponding Japanese word katta.

a distortion model should estimate phrases including katta as a source phrase position to be translated next.


And it also turns out that the word at the CP alone is not enough to estimate the NP.

not only the word at the CP but also the word at a NP candidate (NPC) should be considered simultaneously.


The difference is caused by the words surrounding the NPCs (context), the CP context, and the words between the CP and the NPC.

these should be considered when estimating the NP.


In Equation 1, i, j, and S are used by the feature functions.

equation 1 can utilize features consisting of both s i , which is the word specified at i, and s j , which is the word specified at j, or both the context of i and the context of j simultaneously.


For example, many Chinese words can act as a verb, noun and adjective without any change, while their English counter parts are distinct words with quite different word embeddings due to their different syntactic roles.

we have to modify the word embeddings in subsequent steps according to bilingual data.


Finally, from the probability of the word alignments, we can determine whether the segments are parallel.

our model will attempt to find the optimal values for the segments [p, q] [u, v], languages l, r and word alignments a jointly.


The main bottleneck of the naive algorithm is finding new Viterbi Model 1 word alignments every time we change the spans.

we propose an iterative approach to compute the Viterbi word alignments for IBM Model 1 using dynamic programming.


Since, the update is linear and we need to iterate through all tokens twice, this update takes O(n 3 ) operations.

the update λ +v is applied for the cases with three degrees of freedom and a linear update, it runs in O(n 4 ) time.


Furthermore, based on the work in (Jelh et al., 2012), if a tweet A is identified as a retweet, meaning that it references another tweet B, we also consider the hypothesis that these tweets may be mutual translations.

if A and B contain trigrams in different languages, 1 http://open.weibo.com/wiki/API文档/en these are also considered for the extraction of parallel data.


Our method needs to determine if a given tweet contains parallel data, and if so, what is the language pair of the data, and what segments are parallel.

we had a native Mandarin speaker, also fluent in English, to annotate 2000 tweets sampled from crawled Weibo tweets.


One important question of answer is what portion of the Microblogs contains parallel data.

we also use the random sample Twitter and annotated 1200 samples, identifying whether each sample contains parallel data, for the EN-ZH and AR-EN filtered tweets.


In the other extreme, setting σ 1 = 0 simply ignores task #1's training data.

the optimal v 1 will be a vector of zeros; since tasks #2 and #3 have no parts in common, the objective will decouple into a sum of two independent terms 6 Note that, by substituting u k := w + v k and solving for w, the problem in Eq.


The number of states in the dynamic programming lattice grows exponentially with the order of the language model (Jelinek, 1998;Koehn, 2004).

inference can become slow when the language model order n is large.


In the early iterations of EM, our font parameters are still inaccurate, and to prune heavily based on such parameters would rule out correct analyses.

we gradually increase the aggressiveness of pruning over the course of EM.


For the navigation task, this supervision consists of the observed sequence of actions taken by a human when following an instruction.

it is impossible to directly apply conventional discriminative reranking to such problems.


(2011) produces explosively large grammars when applied to more complex problems, such as our navigation task.

kim and Mooney enhanced their approach to use a previously learned semantic lexicon to reduce the induced grammar to a tractable size.


Instead, our modified model replaces the gold-standard reference parse with the "pseudo-gold" parse tree  end for 9: end procedure whose derived MR plan is most successful at getting to the desired goal location.

the third component in our reranking model becomes an evaluation function EXEC that maps a parse tree y into a real number representing the success rate (w.r.t.


However, in the navigation task, the ultimate goal is to generate a plan that, when actually executed in the virtual environment, leads to the desired destination.

the pseudo-gold reference is chosen as the candidate parse that produces the MR plan with the great-est execution success.


The final plan MRs are produced from parse trees using compositional semantics (see Kim and Mooney (2012) for details).

the n-best parse trees for the baseline model do not necessarily produce the n-best distinct plans, since many parses can produce the same plan.


This results in a very large, sparse feature space which can easily lead the reranking model to over-fit the training data and prevent it from generalizing properly.

we also tried constructing more general features that are less sparse.


Categories are annotated over the graph's edges, and represent the descendant unit's role in forming the semantics of the parent unit.

the internal structure of a unit is represented by its outbound edges and their categories, while the roles a unit plays in the relations it participates in are represented by its inbound edges.


However, this is very difficult given the large number of potential candidates, especially those with slight variations.

following the Concept Definition Retrieval task in (Guo and Diab, 2012b) and (Steck, 2010) we use a metric for evaluating the ranking of the correct news article to evaluate the systems, namely, ATOP t , area under the TOPK t (k) recall curve for a tweet t. Basically, it is the normalized ranking ∈ [0, 1] of the correct news article among all candidate news articles: ATOP t = 1 means the url-referred news article has the highest similarity value with the tweet (a correct NARU); ATOP t = 0.95 means the similarity value with correct news article is larger than 95% of the candidates, i.e.


Similarly a word w i is generalized by P •,i .

the inner product of a word vector P •,i and a short text vector Q •,j is to approximate the cell X ij (shaded part in Figure  2).


However, we cannot simply assume any two tweets are similar only based on the timestamp.

for a tweet we link it to the k most similar tweets whose published time is within 24 hours of the target tweet's timestamp.


This is understandable since by introducing author links between tweets, to some degree we are averaging the latent vectors of tweets written by the same person.

for a tweet whose topic is vague and hard to detect, it will get some prior knowledge of topics through the author links (hence increase ATOP), whereas this prior knowledge becomes noise for the tweets that are already handled very well by the model (hence decrease TOP10 and RR).


Adopting Kaplan (1999)'s metaphor, these are the coin of the realm when it comes to paying the addressee respect.

they are indicators of positive politeness.


3 However, an exact solution to this optimization problem is computationally expensive.

we find a local maximum by employing the simulated annealing algorithm (Kirkpatrick et al., 1983), altering one parameter at a time to optimize F-score by holding the remaining parameters fixed.


Contrary to what one might expect, sign languages are not artificial languages, but natural languages that spontaneously arose within deaf communities; although they are typically named after the region where they are used, they are not derived from the local spoken language and tend to bear no similarity to it.

translation from any spoken language into the signed language of that specific region is at least as complicated as between any pairs of unrelated languages.


After the last iteration, we use these collected counts as weights and compute maximum directed spanning trees using the Chu-Liu/Edmonds' algorithm (Chu and Liu, 1965).

the resulting trees consist of edges maximizing the sum of individual counts: It is important to note that the MST algorithm may produce non-projective trees.


However, this computation is costly for each sentence, and has to be repeated for all the sentences.

instead, we assume that the distribution over y CF G is a loglinear model with parameters θ CF G (i.e., a subvector of θ) , namely: (3) we assume that both p joint and p CF G have the same dependence on the f CF G features.


However, we limit the features to be designed locally in a derivation in order to run inside-outside efficiently.

we use the preceding and succeeding POS tag information to approximate the grandparent and sibling information.


Our approach employs a sophisticated Bayesian non-parametric prior, namely the hierarchical Pitman-Yor Process (Teh, 2006; to represent backoff from larger to smaller contexts.

we need only use very simple translation units -primarily single words, but can still describe complex multi-word units through correlations between their component translation decisions.


Note that GIZA++ has linked many function words which should be left unaligned, by using rare English terms as garbage collectors.

this only allows for the extraction of few large phrase-pairs (e.g.


In fact, over the full dataset, the parameter values we observe are θ + w = 93 168549 = 5.70e-4 and θ − w = 263 564717 = 4.65e-4, with a ratio of θ + w θ − w = 1.223.

in actuality, the word "resources" is a mild positive indicator of the Earnings class.


Further, the maximum likelihood estimate for θ − w (based on an occurrence count of 2 out of 547 observations) is somewhat more reliable than that for θ + w (1 of 216 observations).

θ + w is adjusted upward relatively more than θ − w via MNB-FM's constrained ML estimation.


However, the context space generated in Section 4.2 may be too sparse to find numbers in the database, especially when a query context is fine-grained.

when no item is retrieved for the query context, we employ a backoff strategy to drop some of the uninformative elements in the query context: elements are dropped from the context based on the type of argument, in this order: he (prep to), kara (prep from), ha (nsubj), yori (prep from), made (prep to), nite (prep at), de (prep at, prep by), ni (prep at), wo (dobj), ga (nsubj), and verb.


Unlike in most applications of HMMs in text processing, in which the representation of a token is simply its word or lemma identity, tokens in DSHMM are also associated with a vector representation of their meaning in context according to a distributional semantic model (Section 3.1).

the emissions can be decomposed into pairs H t = (lemma(H t ), sem(H t )) and A ta = (lemma(A ta ), sem(A ta )), where lemma and sem are functions that return the lemma identity and the semantic vector respectively.


Afterwards, the contextualized vector in the original space, c, can be transformed into a vector in the reduced space, c R , by c R = Σ −1 k V T k c. Distributional semantic vectors are traditionally compared by measures which ignore vector magnitudes, such as cosine similarity, but a multivariate Gaussian is sensitive to magnitudes.

the final step is to normalize c R into a unit vector by dividing it by its L2 norm, || c R ||.


We also compute another feature av-eragePercentageOfTranslatedWords which builds the average between the feature values of percent-ageOfTranslatedWords from source to target and target to source.

in total we have 13 dictionary based features.


We observed that all the missing term pairs were not cognates.

the only way these terms could be recognized as positive is if they are found in the GIZA++ dictionaries.


17% of the word-word term pairs or 9% of the overall set of aligned term pairs) were already in either the EN-DE or DE-EN GIZA++ dictionaries.

of our newly extracted term pairs a relatively small proportion are rediscovered dictionary entries.


Word clustering is a powerful mechanism to "transfer" a sentiment classifier from one language to another.

can be used in truly resource scarce scenarios like that of English-Marathi CLSA.


Unlike dependency parsing, constituent parse trees for the same sentence can have different numbers of nodes, mainly due to the existence of unary nodes.

completed state items for the same sentence can have different numbers of unary actions.


Note, however, that we have constructed a highly mismatched case where we train on DS1 (that supports just 8 routes) and test on DS2 (that supports 40 routes).

many route, origin and destination slot values in the test data do not appear in the training data.


but, although) were selected out from raw corpora using a patternbased approach and then these words or phrases were removed from these sentences.

the resulting sentences were used as synthetic training examples for implicit discourse relation recognition.


We see that there should be some linguistical similarities between explicit and implicit discourse examples.

the first question arises: can we exploit this kind of linguistic similarity between explicit and implicit discourse examples to improve implicit discourse relation recognition?


Obviously, the data generated from the second source is cleaner and more reliable than that from the first source.

the second question to address in this work is: whether synthetic implicit discourse data generated from explicit discourse data source (i.e., the second source) can lead to a better performance than that from raw corpora (i.e., the first source)?


Specifically, the ASO method adopted singular value decomposition (SVD) to obtain Θ and m predictors that minimize the empirical risk summed over all the m problems.

the problem of optimization becomes the minimization of the joint empirical risk written as: where loss function L(.)


If the hypothesis is correct, the performance of main task would be improved by auxiliary tasks created from synthetical implicit data.

in the experiments of multi-task learning, only natural implicit examples (i.e., imp) data are used for main task training while different combinations of synthetical implicit examples (exp and BLLIP) are used for auxiliary task training.


Although our experiments show that synthetic implicit data can help implicit discourse relation classification under multi-task learning framework, the overall performance is still quite low (44.64% in F 1 ).

we analyze the types of ambiguity in relations and connectives in order to motivate possible future work.


Given the example E2 in PDTB, the PDTB annotators explain it as Contingency or Expansion relation and manually insert corresponding implicit connective for one thing or because to express its relation.

arg1:Now the stage is set for the battle to play out arg2:The anti-programmers are getting some helpful thunder from Congress Connective1:because Sense1:Contingency.Cause.Reason Connective2:for one thing Sense2:Expansion.Instantiation (wsj 0118) the ambiguity of implicit discourse relations makes this task difficult in itself.


In this kind of architecture, the performance of the entire coreference system strongly depends on the quality of the local pairwise classifier.

1 a lot of research effort on coreference resolution has focused on trying to boost the performance of the pairwise classifier.


This result can be extended easily to the case of n feature spaces.

with a deterministic separation of the data, a large model can be learned using smaller independent models.


As for training, the data on which a classifier at a given node is evaluated is the same as the union of all data used to evaluate the classifiers corresponding to the children of this node.

we are able to compare the scores obtained at a node to the "union of the scores" obtained at its children.


Each of these decoders is typically (although not always) used in tandem with a specific sampling selection at training.

closest-First for instance is used in combination with a sample selection that generates training instances only for the mentions that occur between the closest antecedent and the anaphor (Soon et al., 2001  We evaluated the system on the English part of the corpus provided in the coNLL-2012 Shared Task (Pradhan et al., 2012), referred to as coNLL-2012 here.


20 Table 1 shows that Latin and Ancient Greek treebanks have on average more than 6 CSs per 100 tokens, more than 2 conjuncts per CS, and Latin has also the highest number of shared modifiers per CS.

the percentage of nodes affected by the roundtrip is the highest for these languages and the lower roundtrip UAS is not surprising.


In Table 1 we show some nontrivial generalized patterns together with matching HTML text snippets.

of step 3, we obtain a glossary G k for the terms discovered at iteration k. Step 4.


The analyses in ParGram-Bank are the output of computational LFG Par-Gram grammars.

of ParGram's centrally agreed upon feature sets and prototypical analyses, the representations are not only deep in nature, but maximally parallel.


However, they are far from containing only interesting semantic relations.

improving such thesaurus is an important issue that is mainly tackled indirectly through the improvement of semantic similarity measures.


This co-occurrence can be based either on the position of the word in the text in relation to the entry or on the presence of a syntactic relation between the entry and the word.

the distributional representation of a word takes the unstructured form of a bag of words or the more structured form of a set of pairs {syntactic relation, word}.


A step further, such classifier can be viewed as a means for testing whether or not a word has the same meaning as E. This is a problem close to WSD as it is performed in the context of the pseudo-word disambiguation paradigm (Gale et al., 1992): a pseudo-word is created with two senses, E and notE, notE corresponding to one or several words that are supposed to be representative of a meaning different from the meaning of E. The objective is then to build a classifier for distinguishing the pseudo-senses E and notE.

of this view, we adopt the same kind of features as the ones used for WSD for building our classifier.


It is an interesting result since the features upon which this classifier was built were taken from WSD and were not specifically selected for this task.

there is probably some room for improvement.


The building of distributional thesaurus is generally viewed as an application or a mode of evaluation of work about semantic similarity or semantic relatedness.

the improvement of such thesaurus is generally not directly addressed but is a possible consequence of the improvement of semantic similarity measures.


This is an important difference with the method presented in this article, which mainly exploits the context of the occurrences of words in the corpus used for the building the initial thesaurus.

at a global scale, our reranked thesaurus outperforms the final thesaurus of (Ferret, 2012) for nearly all measures.


Participants were asked to list up to 14 different properties that describe a concept.

the attributes of a concept denote the set of properties humans consider most salient.


This is important because it decouples FST development and the final POS task.

any FST for the language, regardless of its provenance, can be used with any target POS tagset.


This difference is due to the fact that the type annotations started with the most frequent words whereas the token annotations were on random sentences.

type annotations quickly cover a significant portion of the language's tokens.


low-resource languages, the amount of digitized text, such as transcripts or websites, is very limited and may, in fact, require substantial effort to accumulate, even with assistance from computational tools (Bird, 2011).

the collection of raw data can be considered another time-sensitive task for which the tradeoffs with previously-discussed annotation efforts must contend.


Furthermore, using external name translation table only did not improve translation quality in most test sets except for BOLT2.

it is important to use name-replaced corpora for rule extraction to fully take advantage of improved word alignment.


All mappings φ, with φ(f ) = f for any f ∈ A and f ∈B will induce a score of −∞ since for f ∈Ã all N f f > 0 and S f f = −∞ for f ∈B.

any φ with score > −∞ will fulfill φ(f ) ∈B for f ∈B.


Further, by enumerating all six possible permutations, it can be seen that only the φ with φ(f ) = f for f ∈B induces a score of > −∞.

we can rewrite to Here • (AB) is independent of φ since ∀f ∈Ã, f ∈ {n + 1, n + 3} : ∀f ∈Ã : • (BA) is independent of φ since ∀f ∈Ã, f ∈B : • (BB) is independent of φ.


Thus we can rewrite to Here • (AB) is independent of φ since ∀f ∈Ã, f ∈ {n + 1, n + 3} : ∀f ∈Ã : • (BA) is independent of φ since ∀f ∈Ã, f ∈B : • (BB) is independent of φ.

with some constant c, we can finally rewrite Equation 28 as Inserting the definition of N f f and S f f (simplified using constants c , and c ) we obtain which is equivalent to the original quadratic assignment problem we have shown that a solution to the quadratic assignment problem in Equation 34 is a solution to the decipherment problem in Equation 20 and vice versa.


Nevertheless, most existing approaches to sentence alignment follow the monotonicity assumption that coupled sentences in bitexts appear in a similar sequential order in two languages and crossings are not entertained in general (Langlais et al., 1998;Wu, 2010).

the task of sentence alignment becomes handily solvable by means of such basic techniques as dynamic programming.


Unfortunately, this method requires that we set K manually, and extract relations for each given relation.

this is unsuitable to support general relations.


A brute-force statement matching approach often fails due to ambiguity created by ignoring context, and missing information in T N or T R .

we detect similar document pairs to boost the statement matching process.


Traditional sentiment is mainly expressed with sentiment terms (e.g., great and bad), while agreement and disagreement are inferred by AD-expressions (e.g., I agree and I disagree), which we also call AD-sentiment expressions.

this work expands the sentiment analysis research.


This is nothing wrong per se because the statistical tests only judge significance of an ngram, but a significant n-gram may not necessarily be relevant in a given problem domain.

the existing approaches have some major shortcomings for our task.


Continuing from the previous example, given the expression type = , 2 is relevant while 1 is not as "government" and "disagree" are highly unlikely and likely respectively to be clustered in = .

we want to rank phrases based on ( = 1| , ) where denotes the expression type (Agreement/Disagreement), denotes a candidate phrase.


We find that choosing top k = 5000 candidate ngrams based on our proposed ranking, we obtain a coverage of 87% for agreement and 89.64 for disagreement expression types which are reasonably good.

we choose top 5000 candidate n-grams for each expression type and add them to the vocabulary beyond all unigrams.


The evaluation of this task requires human judges to read all the posts where the two users forming the pair have interacted.

it is hard to evaluate all 1461 pairs in our dataset.


People use metaphor-rich language to express affect and often affect is expressed through the usage of metaphors.

understanding that the metaphor "I was boiling inside when I saw him."


Once a label has been assigned to a word, the ZRUG ¶VDVVRFLDWHGYHFWRU is edited so it will have the value of 1 for each attribute present in its newly assigned MSD.

of encoding each individual attribute separately for MSDs, the tagger can assign new tags (that were never associated with the current word in the training corpus).


If one wants to process a large amount of text and is interested only in assigning grammatical categories to words, he can use a MSD encoding in which he strips off all unnecessary features.

the number of necessary neurons would decrease, which assures faster training and tagging.


Consequently, results obtained on multitopic corpora are prone to be biased by the correlation of authors with specific topics.

several authors introduce topic-controlled corpora for applications such as author identification (Koppel and Schler, 2003;Luyckx and Daelemans, 2005) or genre detection (Finn and Kushmerick, 2006).


According to the Wikipedia policies 3 , an article has to be written from a neutral point of view.

authors must avoid stating opinions and seriously contested assertions as facts, avoid presenting uncontested factual assertions as mere opinions, prefer nonjudgmental language and indicate the relative prominence of opposing views.


Depending on the quality assurance processes established in a WikiProject, different importance is given to individual types of flaws.

the distribution of cleanup templates regarding structural or grammatical flaws is also biased towards certain topics.


Without loss of generality, we assume that an article, from which a cleanup template τ ∈ T f is deleted at a point in time d τ , the article no longer suffers from flaw f at that point in time.

the revision r d τ is a reliable negative instance for the flaw f .


In addition to cross-validation, we performed a cross-corpus evaluation of the classifiers for each flaw.

we evaluated the performance of the unbiased classifiers (trained on RELALL) on the biased data (RELP) and vice versa.


In the unbiased case, most of the informative ngrams were flaw specific expressions.

biased classifiers fail on the unbiased dataset in which the positive and negative class are sampled from the same topics, which renders the highly ranked topic ngrams unusable.


For the CWS task, we follow the widely-used BIES coding scheme (Low et al., 2005;Hai et al., 2006), where B, I, E and S stand for beginning of a word, inside a word, end of a word and singlecharacter word, respectively.

we have two (hidden) labels to associate with each (observable) character.


As pointed out by Yeh and Alexander (2000), the randomization method is more reliable in measuring the significance of F 1 through handling non-linear functions of random variables.

we employ Padó (2006)'s implementation of randomization algorithm to measure the significance of F 1 .


More importantly, by jointly optimizing the probabilities of labels on both layers, the FCRF model slightly but significantly improves over the sequential LCRF method, answering RQ2.

we conclude that jointly modeling both tasks is more effective than performing the tasks sequentially.


For example, 'Madonna' is not a reasonable filler for "Who is a better dad, #1 or #2?".

our algorithm employs entity filtering to exclude candidate instantiations that do not make sense.


and "Who is the most beautiful woman in the world, Adriana Lima or Jessica Alba?".

we decided to employ a Conditional Random Fields (CRF) tag-ger (Lafferty et al., 2001) to the task, since CRF was shown to be state-of-the-art for sequential relation extraction (Mooney and Bunescu, 2005;Culotta et al., 2006;Jindal and Liu, 2006).


In our study, uniform negative example sampling turned the training into a trivial task, preventing from the classifier to choose an useful discriminative boundary.

we generate negative examples by sampling only from pairs of named entities that both pass the single entity filter for this relation.


One explanation for this is that sometimes the instantiation filter eliminates all possible entity pairs for some relation that is incorrectly considered relevant by the algorithm.

the filtering of entities provides also an additional filtering perspective on relevance.


The unavailable punctuations and sentence boundaries in transcribed speech texts create barriers to many subsequent processing tasks, such as summarization, information extraction, question answering and machine translation.

the segmentation of long texts is necessary in many real applications.


As each word must be pushed to the stack once and popped off once, the number of actions needed to parse a sentence is always 2n, where n is the length of the sentence.

transitionbased parsing has a linear complexity with the length of input and naturally it can be extended to process the stream of words.


As the words in the stream are consumed, each computation of transition actions is followed by a computation of punctuation prediction.

the two sub-models are computed in a cascaded way, until the optimal parsing tree and optimal punctuation symbols are generated.


Similar to the idea of joint POS tagging and parsing (Hatori et al., 2011;Bohnet and Nivre, 2012), we propose attaching the punctuation prediction onto the parsing tree by embedding into .

we extend the conventional transition actions illustrated in Table 1 to a new set of transition actions for the parsing, denoted by ̂: where Q is the set of punctuation symbols to be predicted, is a punctuation symbol belonging to Q, Shift(s) is an action that attaches s to the current word on the basis of original Shift action in parsing, RightArc(s) attaches to the current word on the basis of original RightArc action.


However, the production of such labeled data is extremely timeconsuming and expensive (Jiao et al., 2006;Jiang et al., 2009).

semi-supervised joint S&T appears to be a natural solution for easily incorporating accessible unlabeled data to improve the joint S&T model.


Feature x 1 x 2 x 3 Type of Trigram: number, punctuation, alphabetic letter and other  The nature of the similarity graph enforces that the connected trigrams with high weight appearing in different texts should have similar syntax configurations.

the constructed graph is expected to provide additional information that cannot be expressed directly in a sequence model (Subramanya et al., 2010).


The modified CRFs model allows us to flexibly define the interaction between estimated state values and virtual evidences by potential functions.

given labeled and unlabeled data, the learning objective is defined as follows: (5) where the conditional probability in the second term is denoted as The first term in Equation (5) is the same as Equation (2), which is the traditional CRFs learning objective function on the labeled data.


Here the gradient of the Q function can be measured by: The forward-backward algorithm is used to measure p(y t−1 i , y t i |x i , v i ; Λ) and p(y t−1 i , y t i |x i ; Λ).

the objective function Equation (5) is optimized as follows: for the instances i = 1, 2, ..., l, the parameters Λ are learned as the supervised manner; for the instances i = l + 1, l + 2, ..., u + l, in the E-step, the expected value of Q function is computed, based on the current model Λ g .


Moreover they include a Metropolis-Hastings correction step, which is required to correct the samples to account for repeated substructures which will be otherwise underrepresented.

their approach does not constitute a Markov Chain Monte Carlo sampler, but rather a complex heuristic.


Its most important advantage is that arbitrary features can be added to the model.

it casts complex translation between a pair of languages as feature engineering, which facilitates research and development for SMT.


Again, we train the parameters (W, W ) with MERT or PRO and get the new parameters (W ,W ).

we can set θ = (W ,W , M, B) as the final solution for Eq.


During decoding, we just look up these scores instead of re-calculating them on the fly.

the decoding efficiency of AdNN-Hiero-E is almost the same as that of L-Hiero.


Today, more parallel sentences are drawn from divergent domains, and the size keeps growing.

how to effectively use those data and improve translation performance becomes a challenging issue.


Second, extracted phrase tables are combined as if they are drawn from a hierarchical Pitman-Yor process, in which the phrase tables represented as tables in the Chinese restaurant process (CRP) are hierarchically chained by treating each of the previously learned phrase tables as prior to the current one.

we can easily update the chain of phrase tables by appending the newly extracted phrase table and by treating the chain of the previous ones as its prior.


Our MBOT rules are obtained automatically from data like that in Figure 3.

we (word) align the bilingual text and parse it in both the source and the target language.


Note that these rules are not necessarily shallow (the last two rules are not).

we post-process the extracted rules and make them shallow.


The language model (LM) scoring is directly integrated into the cube pruning algorithm.

lM estimates are available for all considered hypotheses.


Assembling the string we obtain Offizielle Prognosen sind von nur 3 % ausgegangen which is scored by the LM.

we first score the 4-grams "Offizielle Prognosen sind von", then "Prognosen sind von nur", etc.


A sentence pair observed in the real data is shown in Figure 1 along with the word alignment obtained from an automatic word aligner, where the English subject pronoun "that" is missing on the Chinese side.

"that" is incorrectly aligned to the second to the last Chinese word "De", due to their high co-occurrence frequency in the training data.


The Japanese noun " " in  Example 1 corresponds to the English verb "use", while that in Example 2 corresponds to the English noun "usage".

japanese nouns act like verbs in English in one situation, and nouns in English in another.


The joint model is prone to a data sparseness problem, since each observation is a combination of a source word and its aligned target word.

we propose an independent model, where each hidden state generates a source word and its aligned target word separately.


We cannot directly evaluate the tagging accuracy of the parser trained through Step 3 because we do not have any data with induced POS tags other than the 10,000sentence data gained through Step 2.

we split the 10,000 data into the first 9,000 data for training and the remaining 1,000 for testing, and then a dependency parser was learned in the same way as in Step 3.


Note that the dependency accuracies are measured on the automatically parsed dependency trees, not on the syntactically correct gold standard trees.

original achieved the best dependency accuracy.


For example in Table 1, English words "interest" and "bank" that have multiple meanings under different contexts are correctly addressed by using the state-of-the-art translation tool −−Google Translate.

4 word ambiguity based on contextual information is naturally involved when questions are translated.


, U P fixed, the update of U p amounts to the following optimization problem: Algorithm 1 Optimization framework Input: iK ) T be the column vectors whose entries are those of the i th row of D p and U p respectively.

the optimization of equation 5can be decomposed into M p optimization problems that can be solved independently, with each corresponding to one row of U p : Equation 6is a standard least squares problems in statistics and the solution is: Algorithm 2 shows the procedure.


if p ∈ [2, P ], the objective function can be written as: if p = 1, the objective function can be written as: j be the j th column vector of D p , and v (p) j be the j th column vector of V p , respectively.

equation (8) can be rewritten as: (10) which can be decomposed into N optimization problems that can be solved independently, with each corresponding to one column of V p : Equation 12is a least square problem with L 2 norm regularization.


So we construct a dummy translator (DT) that translates an English word to itself.

through this translation, we do not add any semantic information into the original questions.


The reason is that GTrans is context-dependent and thus produces different translated Chinese words depending on the context of an English word.

the word ambiguity problem can be solved during the English-Chinese translation.


With this representation we can write: It can be shown that the first term in equation 4 increases with the quality of the selected items, and the second term increases with their diversity.

this distribution places most of its weight on sets that are both high quality and diverse.


In contrast, at the lower left of the figure, where perplexity is small (< 30) and bandwidths has been narrowed at iteration 101-500, correct change occupied only 32% of the whole.

we can conclude that if sufficiently random samples of nearby words are provided, our smoothing model is reliable, though it is trained in an unsupervised fashion.


The final type of the phrase S[dcl] indicates a sentence and hence a complete CCG proof.

at each point in a CCG parse we can deduce the possible next steps in the derivation by considering the available types and combinatory rules.


Note that our amortization schemes guarantee optimal solution.

using amortization, task accuracy remains the same as using the original solver.


(e.g., "scary" and "spooky", which have similar context features, go into the same semantic tag, "genre").

to best of our knowledge, MTR is the first topic model to incorporate word features while considering the sequence of words.


Motivated by (Lavoie et al., 2011), we want the loss function to have a dependency on the prior model predictions.

r-SSL encodes the history of the prior pre-Algorithm 2 retrospective Semi-Supervised CrF Input: Labeled U l , and unlabeled U u data.


(⇒) v has an edge in either I or J and an edge e outside both I and J.

it must be a boundary node of either I or J.


For e J (because I and J are disjoint), and if e ′ ∈ J, then v would be a boundary node of J.

e ′ I ∪ J, so v is a boundary node of I ∪ J.


In the Nonterminal and Binary inference rules, each boundary edge could belong to I or J or neither.

the number of possible instantiations of any inference rule is in O ((3 d n) k+1 ).


While their approach completely obviates the need for direct supervision, their target logic forms are selfinduced clusters, which do not align with existing database or ontology.

uSP can not be used directly to answer complex questions against an existing database.


The selected utterances were removed from the corpus during training.

we obtained 400 utterance-response pairs (= 80 utterance-response pairs × 5 workers).


The transcription of the utterance was also made available.

the annotation process included all three modalities: visual, acoustic, and linguistic.


Previous research shows little agreement about the number and types of the basic emotions (Ortony and Turner 1990;Izard 1971).

we assume that the number and types of basic emotions are hidden and not pre-defined and propose a Probabilistic Sense Sentiment Similarity (PSSS) approach to extract the hidden emotions of word senses to infer their sentiment similarity.


This could indicate that the bridged model is more accurate than the series model to estimate the number of emotions.

based on the above discussion, the estimated number of emotions is six in our development dataset.


They also tend to incorporate hand-crafted lists of search terms to filter irrelevant content and use sentiment analysis lexicons for extracting opinion bias.

they are quite often restricted to a specific application and therefore, generalise poorly to new data sets .


This is not surprising because of the different task definition.

for the rest of the study on data size impact, we only use data set A from the TAC data and the DUC data as the training set.


We have already seen for the previous ILP method, when using our bigrams together with the weights, better performance can be achieved.

we ask the question whether this is simply because we use supervised learning, or whether our proposed regression model is the key.


Hence, cost(T ) = h t (S) + u∈O\S d(u, S).

let smt(S) denote the cost of a minimum Steiner tree of S. cost(T ) ≥ smt(O ∪ S).


Then, the cover value of the set S is defined as cov(S) = u∈S min(cov(u, S), 0.25cov(u, U )).

3 the final coverage function is: g(S) = w(S) + α B∈B |S ∩ B| 1/2 + β D∈D |S ∩ D| 1/2 + λcov(S), where α, β, λ are non-negative constants.


This property is suitable for summarization purposes, because the gain of adding a new sentence to a summary that already contains sufficient information should be small.

many studies have formalized text summarization as a submodular maximization problem (Lin and Bilmes, 2010;Lin and Bilmes, 2011;Morita et al., 2011).


However, the cost of a union of subtrees from different sentences is simply the sum of the costs of subtrees, while the cost of a union of subtrees from the same sentence is smaller than the sum of the costs.

the problem can be represented as follows: For example, if we add a subtree t containing words {w a , w b , w c } to a summary that already covers words {w a , w b , w d } from the same sentence, the additional cost of t is only c({w c }) because w a and w b are already covered 1 .


Since, from the requirements, the union of valid subtrees is also a valid subtree in the valid set, the model can extract one or more subtrees from one sentence, and generate a compressed sentence by merging those subtrees to generate an equivalent subtree.

the joint model can extract an arbitrarily compressed sentence as a subtree without enumerating all candidates.


sentence compression can be represented as edge pruning.


There are at most (C2 |O| ) MDSs that the algorithm needs to store at each node.

the total computational complexity is O(N C 2 2 2|O| ).


None of the tests were close to significant; the p-values were all above 0.5.

our experiments provided no evidence that the samplers produced different distributions over trees, although it's reasonable to expect that these distributions do indeed differ.


Without any human-created resources, we cannot always determine the adjoin positions correctly.

as a compromise, we wound up implementing approximate heuristic rules to determine the adjoin positions.


In the case shown in the figure, S base \S is assigned for " /PAST-BASE" and S cont \S for " /not-CONT".

of this restriction, we can obtain conditions for every auxiliary agglutination because the two form values in S\S are both restricted after applying combinatory rules (Sec.


In our experiments, a greedy parser performs as accurately as a parser that uses beam search for about 64% of time.

it is preferred if the beam size is not fixed but proportional to the number of low confidence predictions that a greedy parser makes, in which case, fewer transition sequences need to be explored to produce the same or similar parse output.


The range 1-5 shows results of 5 ADAGRAD iterations before bootstrapping, the range 6-9 shows results of 4 iterations during the first bootstrapping, and the range 10-14 shows results of 5 iterations during the second bootstrapping.

the number of bootstrap iteration is 2 where each bootstrapping takes a different number of ADAGRAD iterations.


Take Figure 2 as an example, following the Direct Projection Algorithm (DPA) (Hwa et al., 2005) (Section 5), the dependency relationships between words can be directly projected from the source Algorithm 2 Training projection model ∇φ ← grad(DP , DN , φ(λ)) 4: λ ← climb(φ, ∇φ, λ) 5: until maximization language to the target language.

we can hardly obtain a treebank with complete trees through direct projection.


Because human translation in general preserves semantic equivalence, bi-texts represent two perspectives on the same semantic content (Burkett et al., 2010b).

we can find complementary cues in the two languages that help to disambiguate named entity mentions (Brown et al., 1991).


For example, the target "Chen Guangcheng" only appears once in Weibo.

the co-occurrence of a morph and its target is quite low in the vast amount of information in social media.


We can see that they include similar topics, events (e.g., "fell from power", "gang crackdown", "sing red songs"), and semantic relations (e.g., family relations with "Bo Guagua").

if we can automatically extract and exploit these indicative semantic contexts, we can narrow down the real targets effectively.


Unfortunately the stateof-the-art techniques for these tasks still perform poorly on social media in terms of both accuracy and coverage of important information, these sophisticated semantic links all produced negative impact on the target ranking performance.

we limited the types of vertices into: Morph (M), Entity(E), which includes target candidates, Event (EV), and Non-Entity Noun Phrases (NP); and used co-occurrence as the edge type.